Semantics of Shame in Social Media Discussions of Reality TV Fans

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# Semantics of Shame in Social Media Discussions of Reality TV Fans
### DH2019, Utrecht (NL) July 11, 2019

.pull-left.left[ ### Radim Hladík  
National Institute of Informatics (Japan)  
Institute of Philosophy of the Czech Academy of Sciences (Czech Republic)
##### Contact
radim.hladik@fulbrightmail.org  
@hlageek

##### Acknowledgement
Grant-in-Aid for JSPS Fellows no. 17F17769.
]

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### Markéta Štěchová
Charles University (Czech Republic)
<br><br><br>
##### Contact
stechova@fsv.cuni.cz  
@marquetstech
##### Acknowledgement
Czech Science Foundation no. 17-02521S.
]

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# Reality TV and social stratification

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- **“Demotic turn”** means that media content becomes more representative of non-elite members of society (Turner 2010)

- Even with increased working class presence, __upper classes remain overrepresented__ (Stiernstedt and Jakobsson 2017)

- Class distinctions expressed through __cultural tastes and lifestyle differences__ (Piper 2004; Matheson 2007) or as supposedly __individual moral shortcomings__ (Hirdman 2016)

- Working-class participants systematically become an object of __ridicule or controversy__ (Eriksson 2016; De Benedictis et al. 2017)

]

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# _Wife Swap_ 
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+ British Reality TV show first broadcasted in 2003 (ran for 7 years)
+ Premise: Two wives exchange their homes for one week and both receive a reward for completion of the required swap
+ Czech license acquired by a private broadcaster TV Nova in 2005
+ 10 seasons of the Czech edition by 2018

]

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+ criticized for __"middle-class gaze"__ (Lyle 2008)

- working class as the abject other
  - spectacle of poverty

+ emotions and representend inequalities trigger __"political talk" in online discussion forums__ (Graham 2012)

]

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# Researching the targets of "shaming"

## RQ: What are the targets of shaming practices among the fans of the show on Facebook?

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- shame = an individual emotion
- shaming = enforcement of norms through the generation of negative collective affect and public identification of a trespasser (i.e.  attempt of the collective to induce shame) 
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## Approach

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1. Data
2. Sentiment classification
3. Word embeddings and similarity vectors
4. Interpret differences
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# Data
acquire and clean
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# Data

+ Facebook Page of the show https://www.facebook.com/vymenamanzelek/

+ 5-years worth of “postings” of all available types: posts by page (n=1273), comments (n=26383) and replies (n=28459) from January 2012 to March 2017 
+ scraped from Facebook Graph API

+ processed (e.g. lemmatization) by NLP tools for the Czech languge

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]

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# Sentiment classification
separate comments with negative polarity
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# Sentiment classification task

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+ the original intention was to use existing tools:
  - __Czech SubLex__ 1.0
     <br>
     Czech subjectivity lexicon of 4626 evaluative items
  - __Neural Monkey__
  <br>
  End-to-end deep learning model for machine translation, sentiment analysis and automatic image captioning in Czech
- failed to provide desired levels of accuracy on our dataset
- tested first on a pilot of 150 comments, eventually we manually coded 1300 comments

]

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Ground truth (n = 1300)

| classifier | metric | value
| :-- | :-- | --: | 
| one rule baseline | accuracy | 0.61
| lexicon | accuracy | 0.65 
| | kappa | 0.28
| neural monkey | accuracy | 0.57
| | kappa | 0.16 
| bespoke | accuracy | 0.73 
| | kappa | 0.44 
]

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# Bespoke sentiment classifier

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+ Features
 - Total words count
 - Negative words count
 - Neutral words count
 - Positive words count
 - Negations count
 
+ N-grams
 - unigrams
 - bigrams
 - LSA transformed to 50 dimensions

+ Algorithm
 - Support Vector Machines with Linear Kernel

]

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# Bespoke sentiment classifier

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+ Features
 - Total words count
 - Negative words count
 - Neutral words count
 - Positive words count
 - Negations count
 
+ N-grams
 - unigrams
 - bigrams
 - LSA transformed to 50 dimensions

+ Algorithm
 - Support Vector Machines with Linear Kernel

]
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# Sentiment  by the type of post and year

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]

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]

???
Negative emotion not necessarily sparked by the page owner.

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# Word embeddings
compare biases
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# Training of word embeddings

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Correlations between the compound "shaming" and the top 100 MFW vectors in the __negative__ and __nonnegative__ subcorpora (n = 750)
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]

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__"Shaming" vector__

???
This result means that there is something going in the semantics of plain shame and shaming.

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# Sanity check with fastText

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]

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# Interpretation
identify and categorize words specific to shaming 
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# Preliminary results

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# Conclusions (also quite preliminary)

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+ shame
  - semantic field of "shame" on the _Wife Swap_ Facebook page is a mixture of poverty, sexuality, race

+ shaming 
  - combination of the semantic field of "shame" and negative sentiment
  - selectively draws from the wider field of shame and disproportionately targets issues of hygiene and concomitant voluntary breach of privacy
  - elements of misogyny

+ Is the concept of "middle-class gaze"a middle-class critique?
]

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# Future work

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+ improve accuracy in the sentiment classification task
  
  - feature engineering
  - hyperparameter tuning
  - larger training set
      - label more comments
      - use pre-existing data from other domains

+ improve reliability of word embeddings

- train more models, e.g. n = 1000, and construct similarity vectors from mean values 
  - bonus: observe in/stablity of cosine similarity values

+ implement clustering as an input to interpretation (?)
]

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# Your turn.

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.pull-right.left[
### Semantics of Shame in Social Media Discussions of Reality TV Fans
##### DH2019, Utrecht (NL) July 11, 2019
##### Radim Hladík
@hlageek
##### Markéta Štěchová
@marquetstech
]

## **https://hlageek.github.io/reports/dh2019**